Unsupervised reconstruction of Building Information Modeling wall objects from point cloud data

Abstract Scan-to-BIM of existing buildings is in high demand by the construction industry. However, these models are costly and time-consuming to create. The automation of this process is still subject of ongoing research. Current obstacles include the interpretation and reconstruction of raw point cloud data, which is complicated by the complexity of built structures, the vast amount of data to be processed and the variety of objects in the built environment. This research aims to overcome the current obstacles and reconstruct the structure of buildings in an unsupervised manner. More specifically, a novel method is presented to automatically reconstruct BIM wall objects and their topology. Key contributions of the method are the ability to reconstruct different wall axis and connection types and the simultaneous processing of entire multi-story structures. The method is validated with the Stanford 2D–3D-Semantics Dataset (2D–3D-S).

[1]  Martin Weinmann,et al.  Comparison of belief propagation and graph-cut approaches for contextual classification of 3D lidar point cloud data , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[2]  Kourosh Khoshelham,et al.  Geometric comparison and quality evaluation of 3D models of indoor environments , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[3]  Stefan Hinz,et al.  Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers , 2015 .

[4]  Renato Pajarola,et al.  Automatic room detection and reconstruction in cluttered indoor environments with complex room layouts , 2014, Comput. Graph..

[5]  Cheng Wang,et al.  Toward better boundary preserved supervoxel segmentation for 3D point clouds , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[6]  Avideh Zakhor,et al.  Fast, Automated, Scalable Generation of Textured 3D Models of Indoor Environments , 2015, IEEE Journal of Selected Topics in Signal Processing.

[7]  Jan Boehm,et al.  Automated 3D Reconstruction of Interiors from Point Clouds , 2010 .

[8]  Pedro Arias,et al.  3D Modeling of Building Indoor Spaces and Closed Doors from Imagery and Point Clouds , 2015, Sensors.

[9]  Patrik Tosteberg.  Semantic Segmentation of Point Clouds Using Deep Learning , 2017 .

[11]  M. Bassier,et al.  Clustering of Wall Geometry from Unstructured Point Clouds Using Conditional Random Fields , 2019, Remote. Sens..

[12]  M. Bassier,et al.  CLUSTERING OF WALL GEOMETRY FROM UNSTRUCTURED POINT CLOUDS , 2019 .

[13]  Marc Pollefeys,et al.  Indoor Scan2BIM: Building information models of house interiors , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[14]  S. M. Iman Zolanvari,et al.  Three-dimensional building façade segmentation and opening area detection from point clouds , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[15]  Markus Vincze,et al.  Fast semantic segmentation of 3D point clouds using a dense CRF with learned parameters , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Segmentation of Large Unstructured Point Clouds Using Octree-Based Region Growing And Conditional Random Fields , 2017 .

[17]  Jan Karlshøj,et al.  Proposing a Central AEC Ontology That Allows for Domain Specific Extensions , 2017 .

[18]  George Vosselman,et al.  Contextual segment-based classification of airborne laser scanner data , 2017 .

[19]  Reinhard Klein,et al.  Automatic reconstruction of fully volumetric 3D building models from oriented point clouds , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[20]  Bo Du,et al.  A Three-Step Approach for TLS Point Cloud Classification , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Silvio Savarese,et al.  3D Semantic Parsing of Large-Scale Indoor Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Pieter Pauwels,et al.  EXPRESS to OWL for construction industry: Towards a recommendable and usable ifcOWL ontology , 2016 .

[23]  Dieter Fritsch,et al.  GRAMMAR-SUPPORTED 3D INDOOR RECONSTRUCTION FROM POINT CLOUDS FOR “AS-BUILT” BIM , 2015 .

[24]  Sergio Escalera,et al.  Segmentation of RGB-D indoor scenes by stacking random forests and conditional random fields , 2016, Pattern Recognit. Lett..

[25]  Reinhard Klein,et al.  Automatic reconstruction of parametric building models from indoor point clouds , 2016, Comput. Graph..

[26]  Renato Pajarola,et al.  Piecewise‐planar Reconstruction of Multi‐room Interiors with Arbitrary Wall Arrangements , 2016, Comput. Graph. Forum.

[27]  Haihong Zhu,et al.  Automatic Indoor Reconstruction from Point Clouds in Multi-room Environments with Curved Walls , 2019, Sensors.

[28]  Enrique Valero,et al.  Automatic Method for Building Indoor Boundary Models from Dense Point Clouds Collected by Laser Scanners , 2012, Sensors.

[29]  Jun Cao,et al.  LiDAR point-cloud mapping of building façades for building energy performance simulation , 2019, Automation in Construction.

[30]  Maarten Vergauwen,et al.  Topology Reconstruction of BIM Wall Objects from Point Cloud Data , 2020, Remote. Sens..

[31]  Fernanda Leite,et al.  6D DBSCAN-based segmentation of building point clouds for planar object classification , 2018 .

[32]  Carl T. Haas,et al.  State of research in automatic as-built modelling , 2015, Adv. Eng. Informatics.

[33]  M. Bassier,et al.  Classification of sensor independent point cloud data of building objects using random forests , 2019, Journal of Building Engineering.

[34]  Manolis I. A. Lourakis,et al.  Toward automated generation of parametric BIMs based on hybrid video and laser scanning data , 2010, Adv. Eng. Informatics.

[35]  Weihua Dong,et al.  Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[36]  Danijel Rebolj,et al.  Automated continuous construction progress monitoring using multiple workplace real time 3D scans , 2018, Adv. Eng. Informatics.

[37]  Jaehoon Jung,et al.  Automated 3D Wireframe Modeling of Indoor Structures from Point Clouds Using Constrained Least-Squares Adjustment for As-Built BIM , 2016, J. Comput. Civ. Eng..

[38]  Hyojoo Son,et al.  Semantic as-built 3D modeling of structural elements of buildings based on local concavity and convexity , 2017, Adv. Eng. Informatics.

[39]  Mattia Previtali,et al.  Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing , 2018 .

[40]  Konrad Schindler,et al.  Joint classification and contour extraction of large 3D point clouds , 2017 .

[41]  Silvio Savarese,et al.  Joint 2D-3D-Semantic Data for Indoor Scene Understanding , 2017, ArXiv.

[42]  Frank Schultmann,et al.  Building Information Modeling (BIM) for existing buildings — Literature review and future needs , 2014 .

[43]  Kinjiro Amano,et al.  BIM for existing facilities: feasibility of spectral image integration to 3D point cloud data , 2016 .

[44]  Joon Heo,et al.  Semi-automated approach to indoor mapping for 3D as-built building information modeling , 2015, Comput. Environ. Urban Syst..

[45]  Manolis I. A. Lourakis,et al.  Automated as-built 3D reconstruction of civil infrastructure using computer vision: Achievements, opportunities, and challenges , 2015, Adv. Eng. Informatics.

[46]  Tania Landes,et al.  From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings , 2017 .

[47]  T. Luhmann,et al.  PROBABILISTIC RECONSTRUCTION OF ORTHODOX CHURCHES FROM PRECISIONPOINT CLOUDS USING BAYESIAN NETWORKS AND CELLULAR AUTOMATA , 2017 .

[48]  Markus König,et al.  Interior construction state recognition with 4D BIM registered image sequences , 2018 .

[49]  Enrique Valero,et al.  Detection, Modeling, and Classification of Moldings for Automated Reverse Engineering of Buildings from 3D Data , 2011 .

[50]  Philippe Veron,et al.  REVIEW OF THE "AS-BUILT BIM" APPROACHES , 2013 .

[51]  Dong Chen,et al.  Modeling Indoor Spaces Using Decomposition and Reconstruction of Structural Elements , 2017 .

[52]  Debra F. Laefer,et al.  Slicing Method for curved façade and window extraction from point clouds , 2016 .

[53]  Xiangguo Lin,et al.  Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests , 2017, Remote. Sens..

[54]  Qian Wang,et al.  An Application Oriented Scan-to-BIM Framework , 2019, Remote. Sens..

[55]  John P. Isaacs,et al.  Scan To BIM: The Development Of A Clear Workflow For The Incorporation Of Point Clouds Within A BIM Environment , 2015 .

[56]  Martin Kada,et al.  3D BUILDING RECONSTRUCTION FROM LIDAR BASED ON A CELL DECOMPOSITION APPROACH , 2009 .

[57]  Jan Boehm,et al.  Automatic Geometry Generation from Point Clouds for BIM , 2015, Remote. Sens..

[58]  Antonio Adán,et al.  3D Reconstruction of Interior Wall Surfaces under Occlusion and Clutter , 2011, 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission.

[59]  Debra F. Laefer,et al.  Toward automatic generation of 3D steel structures for building information modelling , 2017 .

[60]  J. Niemeyer,et al.  Contextual classification of lidar data and building object detection in urban areas , 2014 .

[61]  Renato Pajarola,et al.  Bayesian graph-cut optimization for wall surfaces reconstruction in indoor environments , 2017, The Visual Computer.

[62]  Cyrill Stachniss,et al.  Automated 3D volumetric reconstruction of multiple-room building interiors for as-built BIM , 2018, Adv. Eng. Informatics.

[63]  FathiHabib,et al.  Automated as-built 3D reconstruction of civil infrastructure using computer vision , 2015 .

[64]  Leonidas J. Guibas,et al.  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[65]  Antonio Adán,et al.  Semantic scan planning for indoor structural elements of buildings , 2016, Adv. Eng. Informatics.

[66]  Hui Zhao,et al.  Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information , 2018 .

[67]  Sven Oesau,et al.  Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut , 2014 .

[68]  Avideh Zakhor,et al.  Floor plan generation and room labeling of indoor environments from laser range data , 2015, 2014 International Conference on Computer Graphics Theory and Applications (GRAPP).

[69]  Maurice Murphy,et al.  Semi-automatic generation of as-built BIM façade geometry from laser and image data , 2014, J. Inf. Technol. Constr..

[70]  Axel Wendt,et al.  Automatic Room Segmentation From Unstructured 3-D Data of Indoor Environments , 2017, IEEE Robotics and Automation Letters.

[71]  Michela Bertolotto,et al.  Octree-based region growing for point cloud segmentation , 2015 .

[72]  Michael Weinmann,et al.  A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas , 2017, Remote. Sens..

[73]  Fabian Hadiji,et al.  Statistical Relational Learning of Grammar Rules for 3D Building Reconstruction , 2017, Trans. GIS.

[74]  Sisi Zlatanova,et al.  Indoor 3D reconstruction from point clouds for optimal routing in complex buildings to support disaster management , 2020 .

[75]  Allison Kealy,et al.  EXTRACTING TOPOLOGICAL RELATIONS BETWEEN INDOOR SPACES FROM POINT CLOUDS , 2017 .

[76]  Burcu Akinci,et al.  Automatic Creation of Semantically Rich 3D Building Models from Laser Scanner Data , 2013 .

[77]  Juntao Yang,et al.  A probabilistic graphical model for the classification of mobile LiDAR point clouds , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[78]  M. Jancosek,et al.  Flexible building primitives for 3D building modeling , 2015 .

[79]  George Vosselman,et al.  Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories , 2018, Remote. Sens..